How to Scrape Warehouse and Fulfillment Center Availability

The warehouse and fulfillment center market in Southeast Asia is expanding rapidly, driven by e-commerce growth, supply chain diversification, and the shift toward distributed fulfillment networks. For businesses seeking warehouse space, logistics companies expanding their networks, and real estate investors evaluating opportunities, real-time data on warehouse availability, pricing, and capacity is essential.

However, warehouse listing data is scattered across dozens of platforms, brokers, and operator websites, with no single source providing a complete picture. This guide explains how to systematically collect warehouse and fulfillment center availability data across Southeast Asia using proxy infrastructure and automated collection techniques.

The Warehouse Market in Southeast Asia

Market Dynamics

Southeast Asia’s warehouse market has several distinctive characteristics:

Rapid capacity expansion: Countries like Vietnam, Indonesia, and Thailand are seeing significant new warehouse construction, driven by manufacturing relocation from China and growing domestic e-commerce demand.

Tiered markets: Each country has a tiered market structure. In Thailand, for example, Grade A logistics facilities near Laem Chabang command premium rates, while basic warehouses in secondary provinces are significantly cheaper.

E-commerce fulfillment demand: The growth of platforms like Shopee, Lazada, and Tokopedia has created enormous demand for fulfillment centers, particularly in urban areas close to large consumer populations.

Cold chain growth: Rising demand for fresh food delivery and pharmaceutical distribution is driving growth in temperature-controlled warehouse space, which is significantly more expensive and scarcer than dry storage.

Multi-country operations: Companies increasingly need warehouse presence in multiple ASEAN countries, making cross-border warehouse data comparison essential.

Key Data Points

When monitoring warehouse availability, the most valuable data points include:

Location: City, district, proximity to ports, airports, and highways
Size: Available square footage or square meters
Type: Dry storage, cold chain, bonded, free trade zone
Grade: Building quality rating (Grade A, B, C)
Pricing: Rental rate per square meter per month
Minimum lease terms: Minimum commitment period
Amenities: Loading docks, ceiling height, floor load capacity, sprinkler systems
Availability date: When the space becomes available
Operator: Property owner or management company

Data Sources for Warehouse Intelligence

Commercial Real Estate Platforms

Major platforms listing warehouse space in SEA:

JLL, CBRE, Cushman & Wakefield, Colliers: Global real estate firms with SEA warehouse listings
PropertyGuru Commercial: Regional platform with warehouse and industrial listings
99.co: Singapore-focused with industrial property listings
OLX (Indonesia): Listings for warehouse space in Indonesia
DDproperty (Thailand): Commercial property listings including warehouses

Fulfillment Service Marketplaces

Platforms connecting sellers with fulfillment providers:

Locad: Multi-country fulfillment platform across SEA
Anchanto: E-commerce fulfillment services
aCommerce: Regional fulfillment and distribution
Warehouse-specific platforms: Country-specific warehouse marketplaces

Direct Operator Websites

Major warehouse operators with their own listing pages:

GLP: Largest logistics real estate developer in Asia
Mapletree Logistics Trust: Major SEA warehouse REIT
Frasers Logistics: Pan-Asian warehouse operator
Local developers: Country-specific warehouse developers and operators

Why Proxies Are Needed

Geographic Pricing Variations

Warehouse listing platforms may display different information based on the user’s location:

Local pricing: Some platforms show prices only to users from the same country
Currency display: Pricing may be shown in local currency only to local IP addresses
Listing visibility: Some listings may be geo-restricted to potential tenants in the same market
Language: Full listing details may only be available in the local language

DataResearchTools mobile proxies provide local IP addresses in each SEA country, ensuring you see the complete, locally relevant listing data.

Platform Protections

Real estate platforms protect their data to maintain competitive advantage:

Rate limiting: Restrict the number of listings you can view per session
Login requirements: Some detailed pricing data requires account creation
Anti-scraping: JavaScript rendering, CAPTCHA, and behavioral detection
API restrictions: Rate limits on any public APIs

Mobile proxies from DataResearchTools provide the high trust level needed to access these platforms reliably. Real estate platforms see significant legitimate mobile traffic from brokers and tenants checking listings on the go.

Building a Warehouse Data Collection System

Data Model

Define a structured data model for warehouse listings:

from dataclasses import dataclass, field
from typing import List, Optional

@dataclass
class WarehouseListing:
    listing_id: str
    source: str
    country: str
    city: str
    district: str
    address: str
    latitude: Optional[float] = None
    longitude: Optional[float] = None
    total_area_sqm: float = 0
    available_area_sqm: float = 0
    building_grade: str = ""
    warehouse_type: str = ""  # dry, cold_chain, bonded, ftz
    price_per_sqm_monthly: Optional[float] = None
    price_currency: str = ""
    minimum_lease_months: int = 0
    ceiling_height_m: Optional[float] = None
    floor_load_kgsqm: Optional[float] = None
    loading_docks: int = 0
    has_sprinklers: bool = False
    power_capacity_kva: Optional[float] = None
    available_from: str = ""
    operator: str = ""
    contact_info: str = ""
    listing_url: str = ""
    collected_at: str = ""
    amenities: List[str] = field(default_factory=list)

Collection Implementation

class WarehouseDataCollector:
    """Collect warehouse listing data from multiple platforms."""

    def __init__(self, proxy_config):
        self.proxy_config = proxy_config

    def collect_from_platform(self, platform, country, search_params):
        """Collect warehouse listings from a specific platform."""
        proxy = self.proxy_config.get_proxy(country)
        session = requests.Session()
        session.proxies = proxy
        session.headers.update({
            "User-Agent": (
                "Mozilla/5.0 (Linux; Android 14; Samsung SM-S926B) "
                "AppleWebKit/537.36 Chrome/121.0.0.0 Mobile Safari/537.36"
            ),
            "Accept-Language": self._get_language(country),
        })

        listings = []
        page = 1

        while True:
            try:
                response = session.get(
                    platform["search_url"],
                    params={
                        **search_params,
                        "page": page,
                        "property_type": "warehouse",
                    },
                    timeout=30,
                )
                if response.status_code != 200:
                    break

                page_listings = self._parse_listings(
                    response.text, platform, country
                )
                if not page_listings:
                    break

                listings.extend(page_listings)
                page += 1
                time.sleep(random.uniform(3, 6))

            except Exception as e:
                print(f"Error on page {page}: {e}")
                break

        return listings

    def _parse_listings(self, html, platform, country):
        """Parse warehouse listings from HTML."""
        soup = BeautifulSoup(html, "html.parser")
        listings = []

        for card in soup.select(platform["listing_selector"]):
            try:
                listing = WarehouseListing(
                    listing_id=card.get("data-id", ""),
                    source=platform["name"],
                    country=country.upper(),
                    city=self._extract_text(card, platform["city_selector"]),
                    district=self._extract_text(
                        card, platform.get("district_selector", "")
                    ),
                    address=self._extract_text(
                        card, platform.get("address_selector", "")
                    ),
                    available_area_sqm=self._extract_number(
                        card, platform["area_selector"]
                    ),
                    price_per_sqm_monthly=self._extract_price(
                        card, platform.get("price_selector", "")
                    ),
                    price_currency=self._get_currency(country),
                    listing_url=self._extract_url(
                        card, platform.get("link_selector", "a")
                    ),
                    collected_at=datetime.utcnow().isoformat(),
                )
                listings.append(listing)
            except Exception as e:
                print(f"Error parsing listing: {e}")
                continue

        return listings

    def _extract_text(self, element, selector):
        found = element.select_one(selector) if selector else None
        return found.text.strip() if found else ""

    def _extract_number(self, element, selector):
        text = self._extract_text(element, selector)
        cleaned = "".join(c for c in text if c.isdigit() or c == ".")
        try:
            return float(cleaned)
        except ValueError:
            return 0

    def _extract_price(self, element, selector):
        if not selector:
            return None
        text = self._extract_text(element, selector)
        cleaned = "".join(c for c in text if c.isdigit() or c == ".")
        try:
            return float(cleaned)
        except ValueError:
            return None

    def _extract_url(self, element, selector):
        link = element.select_one(selector)
        return link.get("href", "") if link else ""

    def _get_language(self, country):
        langs = {
            "sg": "en-SG,en;q=0.9",
            "th": "th-TH,th;q=0.9,en;q=0.8",
            "id": "id-ID,id;q=0.9,en;q=0.8",
            "vn": "vi-VN,vi;q=0.9,en;q=0.8",
            "ph": "en-PH,en;q=0.9",
            "my": "ms-MY,ms;q=0.9,en;q=0.8",
        }
        return langs.get(country, "en-US,en;q=0.9")

    def _get_currency(self, country):
        currencies = {
            "sg": "SGD", "th": "THB", "id": "IDR",
            "vn": "VND", "ph": "PHP", "my": "MYR",
        }
        return currencies.get(country, "USD")

Collecting Fulfillment Center Data

Fulfillment centers have additional data points beyond traditional warehouses:

@dataclass
class FulfillmentCenterListing:
    """Extended listing data for fulfillment centers."""
    # Inherits warehouse basics
    base_listing: WarehouseListing
    # Fulfillment-specific data
    pick_pack_fee: Optional[float] = None
    storage_fee_per_cbm: Optional[float] = None
    minimum_monthly_orders: int = 0
    supported_platforms: List[str] = field(default_factory=list)
    returns_handling: bool = False
    cod_handling: bool = False
    cross_border_capable: bool = False
    api_integration: bool = False
    sla_processing_hours: int = 0
    coverage_areas: List[str] = field(default_factory=list)

Analyzing Warehouse Data

Price Comparison Across Markets

def compare_warehouse_prices(listings_df):
    """Compare warehouse pricing across countries and cities."""
    # Convert all prices to USD for comparison
    exchange_rates = {
        "SGD": 0.74, "THB": 0.028, "IDR": 0.000063,
        "VND": 0.000040, "PHP": 0.018, "MYR": 0.22,
    }

    listings_df["price_usd"] = listings_df.apply(
        lambda row: (
            row["price_per_sqm_monthly"]
            * exchange_rates.get(row["price_currency"], 1)
        ),
        axis=1,
    )

    comparison = listings_df.groupby(["country", "city"]).agg({
        "price_usd": ["mean", "median", "min", "max", "count"],
        "available_area_sqm": ["sum", "mean"],
    }).round(2)

    return comparison

Availability Trends

def track_availability_trends(historical_df):
    """Track warehouse availability trends over time."""
    trends = historical_df.groupby(
        [historical_df["collected_at"].dt.to_period("W"), "country", "city"]
    ).agg({
        "listing_id": "count",  # Number of available listings
        "available_area_sqm": "sum",  # Total available space
        "price_usd": "median",  # Median price trend
    }).rename(columns={
        "listing_id": "listing_count",
        "available_area_sqm": "total_available_sqm",
        "price_usd": "median_price_usd",
    })

    return trends

Finding Optimal Locations

def find_optimal_warehouse(
    listings_df, requirements, port_proximity_km=50
):
    """Find warehouse listings matching specific requirements."""
    filtered = listings_df[
        (listings_df["available_area_sqm"] >= requirements["min_area_sqm"]) &
        (listings_df["available_area_sqm"] <= requirements.get(
            "max_area_sqm", float("inf")
        )) &
        (listings_df["country"] == requirements["country"])
    ]

    if requirements.get("warehouse_type"):
        filtered = filtered[
            filtered["warehouse_type"] == requirements["warehouse_type"]
        ]

    if requirements.get("max_price_usd"):
        filtered = filtered[
            filtered["price_usd"] <= requirements["max_price_usd"]
        ]

    if requirements.get("building_grade"):
        filtered = filtered[
            filtered["building_grade"].isin(requirements["building_grade"])
        ]

    # Sort by price, then by area match
    filtered = filtered.sort_values(["price_usd", "available_area_sqm"])

    return filtered

Monitoring and Alerting

New Listing Alerts

class WarehouseAlertSystem:
    """Alert when new listings match your criteria."""

    def __init__(self, criteria, notification_service):
        self.criteria = criteria
        self.notifier = notification_service
        self.seen_listings = set()

    def check_new_listings(self, current_listings):
        """Check for new listings matching criteria."""
        alerts = []

        for listing in current_listings:
            if listing.listing_id in self.seen_listings:
                continue

            if self._matches_criteria(listing):
                alerts.append({
                    "type": "NEW_WAREHOUSE_LISTING",
                    "listing_id": listing.listing_id,
                    "location": f"{listing.city}, {listing.country}",
                    "area_sqm": listing.available_area_sqm,
                    "price": listing.price_per_sqm_monthly,
                    "currency": listing.price_currency,
                    "url": listing.listing_url,
                })

            self.seen_listings.add(listing.listing_id)

        if alerts:
            self.notifier.send(alerts)

        return alerts

    def _matches_criteria(self, listing):
        """Check if a listing matches alert criteria."""
        for criterion in self.criteria:
            if listing.country != criterion.get("country"):
                continue
            if listing.available_area_sqm < criterion.get("min_area", 0):
                continue
            if criterion.get("max_price") and listing.price_per_sqm_monthly:
                if listing.price_per_sqm_monthly > criterion["max_price"]:
                    continue
            return True
        return False

DataResearchTools for Warehouse Data Collection

DataResearchTools mobile proxies provide the infrastructure needed for comprehensive warehouse data collection:

Local market access: View warehouse listings with local pricing and full details by connecting through country-specific mobile IPs
Multi-platform coverage: Access real estate platforms, fulfillment marketplaces, and operator websites across all SEA markets
Reliable automation: Mobile proxies maintain consistent access for ongoing monitoring schedules
Scalable collection: Handle thousands of listing pages across multiple countries without access disruptions

Conclusion

Warehouse and fulfillment center data is fragmented across numerous platforms and geographically restricted in ways that make manual monitoring impractical. By building a systematic collection pipeline with DataResearchTools mobile proxies, businesses can gain comprehensive visibility into warehouse availability, pricing, and trends across Southeast Asia.

Whether you are an e-commerce company seeking fulfillment space, a logistics provider expanding your network, or a real estate investor evaluating warehouse opportunities, automated data collection provides the market intelligence needed for confident decision-making. Start with the markets and warehouse types most relevant to your business, and expand your coverage as the system demonstrates its value.